An adapted & improved validation protocol for digital pathology implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Digital Pathology (DP) is transforming disease diagnosis by providing rapid and efficient analysis of tissue samples. However, ensuring the accuracy and reliability of diagnoses is crucial. This manuscript outlines University Health Network (UHN)'s journey towards the development of a customized validation protocol for implementing a digital workflow for primary clinical assessment. Drawing on guidelines from the Royal College of Pathologists (RCPath) UK and the College of American Pathologists (CAP), UHN has tailored its approach to accommodate the unique needs of its 14 subspecialty groups. Our protocol emphasizes pathologist-led self-validation, integration of diverse subspecialty cases, and a phased rollout with continuous monitoring. Additionally, the use of change management principles inspired by Leeds University (CCP) played a critical role in guiding the process, ensuring pathologists' comfort with digital workflows, and addressing subspecialty-specific challenges. This comprehensive validation protocol supports UHN's broader goals of leveraging DP for clinical practice while ensuring patient safety and data integrity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it